Summary of A Robust Three-way Classifier with Shadowed Granular-balls Based on Justifiable Granularity, by Jie Yang et al.
A robust three-way classifier with shadowed granular-balls based on justifiable granularity
by Jie Yang, Lingyun Xiaodiao, Guoyin Wang, Witold Pedrycz, Shuyin Xia, Qinghua Zhang, Di Wu
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The granular-ball (GB)-based classifier introduced by Xia adapts to create coarse-grained information granules for input, enhancing generality and flexibility. However, current GB-based classifiers rigidly assign specific class labels to each instance, lacking strategies to address uncertain instances. To solve this problem, a robust three-way classifier with shadowed GBs is constructed. The enhanced GB generation method combines information entropy with the principle of justifiable granularity. A shadowed mapping partitions GB into Core, Important, and Unessential regions. Based on these shadowed GBs, a three-way classifier categorizes data instances into certain classes and uncertain cases. Comparative experiments on 12 public benchmark datasets show that our model demonstrates robustness in managing uncertain data, mitigates classification risks, and almost outperforms other comparison methods in effectiveness and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to classify data using something called granular-balls (GBs). Right now, these GB-based classifiers are good at making predictions, but they’re not very good at dealing with uncertain or unclear information. To fix this problem, the researchers created a new type of classifier that uses shadowed GBs to deal with uncertainty. They tested their new classifier on lots of different datasets and showed that it can handle uncertainty much better than other methods. This is important because it means we can make more accurate predictions when dealing with complex or unclear data. |
Keywords
* Artificial intelligence * Classification
